@article{KammererHoppenstedtPryssetal.2019,
  author    = {Kammerer, Klaus and Hoppenstedt, Burkhard and Pryss, R{\"u}diger and St{\"o}kler, Steffen and Allgaier, Johannes and Reichert, Manfred},
  title     = {Anomaly Detections for Manufacturing Systems Based on Sensor Data—Insights into Two Challenging Real-World Production Settings},
  series = {Sensors},
  volume    = {19},
  journal   = {Sensors},
  number    = {24},
  issn      = {1424-8220},
  doi       = {10.3390/s19245370},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193885},
  pages     = {5370},
  year      = {2019},
  abstract  = {o build, run, and maintain reliable manufacturing machines, the condition of their components has to be continuously monitored. When following a fine-grained monitoring of these machines, challenges emerge pertaining to the (1) feeding procedure of large amounts of sensor data to downstream processing components and the (2) meaningful analysis of the produced data. Regarding the latter aspect, manifold purposes are addressed by practitioners and researchers. Two analyses of real-world datasets that were generated in production settings are discussed in this paper. More specifically, the analyses had the goals (1) to detect sensor data anomalies for further analyses of a pharma packaging scenario and (2) to predict unfavorable temperature values of a 3D printing machine environment. Based on the results of the analyses, it will be shown that a proper management of machines and their components in industrial manufacturing environments can be efficiently supported by the detection of anomalies. The latter shall help to support the technical evangelists of the production companies more properly.},
  language  = {en}
}
@article{LoefflerWirthKreuzHoppetal.2019,
  author    = {Loeffler-Wirth, Henry and Kreuz, Markus and Hopp, Lydia and Arakelyan, Arsen and Haake, Andrea and Cogliatti, Sergio B. and Feller, Alfred C. and Hansmann, Martin-Leo and Lenze, Dido and M{\"o}ller, Peter and M{\"u}ller-Hermelink, Hans Konrad and Fortenbacher, Erik and Willscher, Edith and Ott, German and Rosenwald, Andreas and Pott, Christiane and Schwaenen, Carsten and Trautmann, Heiko and Wessendorf, Swen and Stein, Harald and Szczepanowski, Monika and Tr{\"u}mper, Lorenz and Hummel, Michael and Klapper, Wolfram and Siebert, Reiner and Loeffler, Markus and Binder, Hans},
  title     = {A modular transcriptome map of mature B cell lymphomas},
  series = {Genome Medicine},
  volume    = {11},
  journal   = {Genome Medicine},
  doi       = {10.1186/s13073-019-0637-7},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-237262},
  year      = {2019},
  abstract  = {Background Germinal center-derived B cell lymphomas are tumors of the lymphoid tissues representing one of the most heterogeneous malignancies. Here we characterize the variety of transcriptomic phenotypes of this disease based on 873 biopsy specimens collected in the German Cancer Aid MMML (Molecular Mechanisms in Malignant Lymphoma) consortium. They include diffuse large B cell lymphoma (DLBCL), follicular lymphoma (FL), Burkitt's lymphoma, mixed FL/DLBCL lymphomas, primary mediastinal large B cell lymphoma, multiple myeloma, IRF4-rearranged large cell lymphoma, MYC-negative Burkitt-like lymphoma with chr. 11q aberration and mantle cell lymphoma. Methods We apply self-organizing map (SOM) machine learning to microarray-derived expression data to generate a holistic view on the transcriptome landscape of lymphomas, to describe the multidimensional nature of gene regulation and to pursue a modular view on co-expression. Expression data were complemented by pathological, genetic and clinical characteristics. Results We present a transcriptome map of B cell lymphomas that allows visual comparison between the SOM portraits of different lymphoma strata and individual cases. It decomposes into one dozen modules of co-expressed genes related to different functional categories, to genetic defects and to the pathogenesis of lymphomas. On a molecular level, this disease rather forms a continuum of expression states than clearly separated phenotypes. We introduced the concept of combinatorial pattern types (PATs) that stratifies the lymphomas into nine PAT groups and, on a coarser level, into five prominent cancer hallmark types with proliferation, inflammation and stroma signatures. Inflammation signatures in combination with healthy B cell and tonsil characteristics associate with better overall survival rates, while proliferation in combination with inflammation and plasma cell characteristics worsens it. A phenotypic similarity tree is presented that reveals possible progression paths along the transcriptional dimensions. Our analysis provided a novel look on the transition range between FL and DLBCL, on DLBCL with poor prognosis showing expression patterns resembling that of Burkitt's lymphoma and particularly on 'double-hit' MYC and BCL2 transformed lymphomas. Conclusions The transcriptome map provides a tool that aggregates, refines and visualizes the data collected in the MMML study and interprets them in the light of previous knowledge to provide orientation and support in current and future studies on lymphomas and on other cancer entities.},
  language  = {en}
}
@article{VeyKapsnerFuchsetal.2019,
  author    = {Vey, Johannes and Kapsner, Lorenz A. and Fuchs, Maximilian and Unberath, Philipp and Veronesi, Giulia and Kunz, Meik},
  title     = {A toolbox for functional analysis and the systematic identification of diagnostic and prognostic gene expression signatures combining meta-analysis and machine learning},
  series = {Cancers},
  volume    = {11},
  journal   = {Cancers},
  number    = {10},
  issn      = {2072-6694},
  doi       = {10.3390/cancers11101606},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193240},
  year      = {2019},
  abstract  = {The identification of biomarker signatures is important for cancer diagnosis and prognosis. However, the detection of clinical reliable signatures is influenced by limited data availability, which may restrict statistical power. Moreover, methods for integration of large sample cohorts and signature identification are limited. We present a step-by-step computational protocol for functional gene expression analysis and the identification of diagnostic and prognostic signatures by combining meta-analysis with machine learning and survival analysis. The novelty of the toolbox lies in its all-in-one functionality, generic design, and modularity. It is exemplified for lung cancer, including a comprehensive evaluation using different validation strategies. However, the protocol is not restricted to specific disease types and can therefore be used by a broad community. The accompanying R package vignette runs in ~1 h and describes the workflow in detail for use by researchers with limited bioinformatics training.},
  language  = {en}
}